Multi-zone interconnected electric heating combined system low-carbon economic dispatching method and system considering carbon emission flow and demand response

Abstract

The invention discloses a multi-zone interconnected electric heating combined system low-carbon economic dispatching method and system considering carbon emission flow and demand response, and the method comprises the steps: analyzing the power flow of a tie line, decomposing a multi-zone interconnected electric heating combined system into a plurality of independent regional subsystems, and analyzing the coordinated coupling relation of each regional subsystem; according to the regional decomposition result, based on a target cascade analysis method, a multi-region interconnected electric heating combined system low-carbon economic dispatching model considering carbon emission flow and demand response is established, and the model comprises an upper-level main problem and lower-level sub-problems corresponding to all regional subsystems; linearizing the model and then solving the model; and calculating a carbon emission allocation result of each regional subsystem according to an optimization result. According to the method, the thermodynamic system heat supply network pipeline and the carbon emission flow of the thermodynamic system heat supply network pipeline are subjected to refined modeling, on the basis, electricity and heat load sides are guided to participate in demand response according to the carbon emission allocation condition while multi-region independent optimization is conducted, data secrecy of subsystems in all regions is facilitated, and low-carbon economic dispatching of the multi-region interconnection electric heating combined system is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optimal scheduling of combined electric-thermal systems, and in particular to a low-carbon economic scheduling method and system for a multi-region interconnected combined electric-thermal system considering carbon emission flow and demand response. BACKGROUND

[0002] With the development of society and the progress of science and technology, the population is growing rapidly, and energy demand, exploitation and consumption are increasing accordingly, which is accompanied by an increase in global carbon emissions. In response to the challenges of global carbon emissions and global climate change, China has proposed the "double carbon" goal. To achieve this goal, the energy industry is the main battlefield, and the power industry is the main force. In the process of achieving low-carbon development in the energy industry, it is of great significance to clarify the carbon emission responsibility and reasonably allocate it. At present, the power system and the heat system are increasingly coupled to form a combined electric-thermal system, and the completion of various ultra-high voltage projects has also made the relationship between regional systems increasingly close, forming a multi-region interconnected system. However, there is currently a lack of fine modeling of the heat network pipeline and its carbon emission flow in the heat system, and it is difficult for multi-regions to independently optimize while guiding the electricity and heat load side to participate in demand response according to the carbon emission allocation, thereby achieving low-carbon economic scheduling of the multi-region interconnected combined electric-thermal system. SUMMARY

[0003] The present application provides a low-carbon economic scheduling method and system for a multi-region interconnected combined electric-thermal system considering carbon emission flow and demand response, which can fine model the heat network pipeline and its carbon emission flow in the heat system, independently optimize multi-regions while guiding the electricity and heat load side to participate in demand response according to the carbon emission allocation, ensure data privacy of regional subsystems, and achieve low-carbon economic scheduling of the multi-region interconnected combined electric-thermal system.

[0004] To achieve the above technical purpose, the present application adopts the following technical solutions:

[0005] A low-carbon economic scheduling method for a multi-region interconnected combined electric-thermal system considering carbon emission flow and demand response, comprising:

[0006] Step 1, analyze the tie-line power flow, decompose the multi-region interconnected combined electric-thermal system into a plurality of independent regional subsystems, each independent regional subsystem is connected with other independent regional subsystems at the boundary node, and the coordination and coupling relationship is analyzed;

[0007] Step 2, based on the target cascade analysis method, a low-carbon economic scheduling model for a multi-region interconnected combined electric-thermal system considering carbon emission flow and demand response is established according to the regional decomposition results, the model includes a superior master problem corresponding to a superior dispatching center and a plurality of subordinate problems respectively corresponding to each regional subsystem;

[0008] Step 3, linearize the model and solve the linearized model;

[0009] Step 4, according to the optimization result, the carbon emission responsibility is allocated, and the carbon emission allocation result of each regional subsystem is obtained.

[0010] The multi-region interconnected electric-thermal combined system is regionally decomposed by line tearing method, and two independent regional subsystems connected with each other are connected through boundary nodes and tie lines; when analyzing the coordination and coupling relationship of each independent regional subsystem in the combined system, each tie line of the regional subsystem is respectively equivalent to a corresponding equivalent generator.

[0011] Further, for the tie line connected between the subsystem k and the subsystem κ, and having the two end nodes being the subsystem k node i and the subsystem κ node j, the tie line flow is as follows:

[0012]

[0013] In the formula, Pti,j represents the active power of the tie line with the first node being the subsystem k node i and the last node being the subsystem κ node j at the t period, Qti,j represents the reactive power of the tie line at the t period; t,k,i , θ t,k,i respectively represent the voltage amplitude and phase angle of the subsystem k node i at the t period; V and V t,k,i , V t,κ,j , θ t,k,i , θ t,κ,j mapping relationship; V and V t,k,i , V t,κ,j , θ t,k,i , θ t,κ,j mapping relationship; V and V t,κ,j , V t,k,i , θ t,κ,j , θ t,k,i mapping relationship; V and V t,κ,j , V t,k,i , θ t,κ,j , θ t,k,i mapping relationship; Ω T represents the scheduling period index set; Ω R represents the electric-thermal combined subsystem index set; represents the boundary node index set of the subsystem k; represents the subsystem index set interconnected with the subsystem k node i; represents the subsystem κ node index interconnected with the subsystem k node i.

[0014] Further, the established multi-region interconnected electric-thermal combined system low-carbon economic dispatching model considering carbon emission flow and demand response, the coordination variables of which include: tie-line active and reactive power, boundary node voltage amplitude and phase angle, boundary node loss, internal loss, tie-line interconnected node loss, loss of loss transferred from the receiving end node to the sending end node, loss of loss transferred from the sending end node to the receiving end node, loss of loss transferred from the receiving end node to the interconnected node, and node tie-line carbon flow density.

[0015] Step 3: the linearized model is solved by iteration, including:

[0016] First iteration: (1) the upper dispatching center sends the initial values of the main problem coordination variables of each regional subsystem to the corresponding regional subsystem; (2) each regional subsystem optimizes the sub-problem of the regional subsystem according to the received initial values of the main problem coordination variables, and uploads the optimized values of the sub-problem coordination variables to the upper dispatching center; (3) the upper dispatching center optimizes the main problem according to the optimized values of the sub-problem coordination variables uploaded by each regional subsystem, and obtains the optimized values of the main problem coordination variables of each regional subsystem; (4) the coefficient of the deviation adjustment term in the optimization objective function is adjusted for the next iteration.

[0017] (τ+1)th iteration: (1) the upper dispatching center sends the optimized values of the main problem coordination variables obtained in the τth iteration to the corresponding regional subsystem; (2) each regional subsystem optimizes the sub-problem of the regional subsystem according to the received optimized values of the main problem coordination variables, and uploads the optimized values of the sub-problem coordination variables obtained in the (τ+1)th iteration to the upper dispatching center; (3) the upper dispatching center optimizes the main problem according to the optimized values of the sub-problem coordination variables uploaded by each regional subsystem, and obtains the optimized values of the main problem coordination variables of each regional subsystem; (4) according to the optimization results of the upper main problem and the lower sub-problem, it is judged whether to converge: if converged, the solution is ended, otherwise the coefficient of the coordination variable deviation adjustment term in the optimization objective function is adjusted for the next iteration; where τ is a natural number greater than 0.

[0018] Further, the lower sub-problem corresponding to each regional subsystem is a double-layer optimization dispatching model, the upper layer of which is a regional subsystem low-carbon economic dispatching model, and the lower layer of which is a load side demand response model; after the upper layer is optimized, the optimized carbon potential values of each load node are transmitted to the lower layer, the lower layer optimizes according to the received carbon potential values of each load node, and the optimized electric and thermal loads are transmitted to the upper layer, and the iteration is solved; when the difference between the current and previous electric and thermal load transfer amounts is less than the error limit, the iteration is ended.

[0019] Furthermore, the optimization objectives of the upper-level subsystem low-carbon economic dispatch model include: coal purchase cost, additional cost of deep peak regulation of units, unit start-up and shutdown cost, carbon trading market transaction cost, green certificate transaction cost, CCER cost, wind curtailment penalty fee, deep peak regulation subsidy, deep peak regulation subsidy sharing cost, demand response cost, interconnection line energy-carbon comprehensive cost and coordination variable deviation adjustment item. The constraints include: node power balance constraint, unit constraint, node voltage constraint, line capacity constraint, thermal system constraint, electric boiler constraint, node network loss constraint, carbon emission flow constraint and interconnection line constraint.

[0020] The load-side demand response model at the lower level has optimization objectives including: energy-carbon comprehensive cost, demand response cost, demand response subsidy, and the constraints are demand response constraints.

[0021] Furthermore, the expression of the combined energy-carbon cost of the interconnection line is:

[0022]

[0023] Where, represents the comprehensive energy-carbon cost of the subsystem k tie line; represents the comprehensive energy-carbon cost of tie line x at node i in subsystem k during period t; represent the electricity price and carbon tax price in period t respectively; represents the active power of tie line x at node i in subsystem k during period t; It represents the network loss transferred from the tie line x of subsystem k node i and the interconnected nodes of the tie line when the subsystem k node i acts as the sending node during period t; represents the carbon flow density of the tie line x of node i in subsystem k during period t; Δt is the scheduling time interval; Ω represents the network loss of the receiving node in subsystem k i transferred to the sending node through the tie line x of subsystem k i; T Represents a set of scheduling period indexes; Represents the subsystem k boundary node index set; represents the index set of the contact line of node i in subsystem k; Ω R Represents the index set of the electric and thermal combined subsystem; The node index set representing the subsystem k that sends power out of the region during period t; The set of tie line indices representing the transmission of power from node i in subsystem k to outside the region during period t; The node index set representing the subsystem k receiving power from outside the area during period t; Represents the set of tie line indices for subsystem k node i receiving power from outside the area during period t.

[0024] Further, the carbon emission flow constraints include: power system node carbon flow balance constraints, heat network pipeline carbon emission flow constraints, heat network mixing point carbon potential constraints, heat source node carbon flow density equality constraints, and heat load node carbon flow density equality constraints.

[0025] Further, step 4 includes: obtaining the optimization results of each variable of each regional subsystem and each tie line by using model solving, and calculating: the carbon flow rate caused by the active power loss that each thermal power unit and heat and power unit needs to bear, the carbon flow rate that the node electric load needs to bear, the carbon flow rate caused by the heat network loss that each heat source needs to bear, and the carbon flow rate that each heat load needs to bear.

[0026] A multi-region interconnected electric-thermal combined system low-carbon economic dispatch system based on the above-mentioned multi-region interconnected electric-thermal combined system low-carbon economic dispatch method considering carbon emission flow and demand response, comprising: a main problem optimization module, a regional data processing module, and a convergence determination module, and each regional subsystem is correspondingly configured with one regional data processing module;

[0027] The main problem optimization module is applied to a superior dispatch center, and is used for: optimizing the main problem according to the sub-problem coordination variable optimization values uploaded by each regional subsystem to obtain main problem coordination variable optimization values of each regional subsystem;

[0028] The regional data processing module comprises: an upper optimization module, a lower optimization module, a carbon flow rate calculation module, and a result output module; the upper optimization module is used for: optimizing the upper subsystem low-carbon economic dispatch model of the lower sub-problem to obtain carbon potential optimization values of each load node; the lower optimization module is used for: optimizing the lower load side demand response model according to the carbon potential optimization values of each load node obtained by the upper layer to obtain electric and heat loads; the carbon flow rate calculation module is used for: calculating the carbon flow rate allocated to each economic subject according to the optimization results; and the result output module is used for outputting the optimization results and the carbon flow rate allocated to each economic subject.

[0029] The convergence determination module is used for determining whether the optimization results converge.

[0030] The present application performs fine modeling on the heat network pipeline and its carbon emission flow of the thermal system, and on this basis, proposes a multi-region interconnected electric-thermal combined system low-carbon economic dispatch method considering carbon emission flow and demand response, which guides the electric and heat load sides to participate in demand response according to the carbon emission allocation while optimizing each regional subsystem independently, guarantees the data security of each regional subsystem, and realizes the low-carbon economic dispatch of the multi-region interconnected electric-thermal combined system. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 The figure is a method flowchart of the method of the present application.

[0032] Figure 2A schematic diagram of a functional module of the system of the present application. DETAILED DESCRIPTION

[0033] The embodiment provides a low-carbon economic dispatching method of a multi-region interconnected electric-thermal combined system considering carbon emission flow and demand response, referring to Figure 1 as shown, comprising the following steps:

[0034] S1: analyzing tie-line flow, decomposing the multi-region interconnected electric-thermal combined system into a plurality of independent regional subsystems, each independent regional subsystem being connected with other independent regional subsystems at a boundary node, and analyzing coordination and coupling relationship.

[0035] The multi-region interconnected electric-thermal combined system can be regarded as a plurality of electric-thermal combined subsystems interconnected through tie lines, and the tie line is connected between a subsystem k and a subsystem κ, and the two end nodes are a subsystem k node i and a subsystem κ node j, and the tie-line flow is as follows:

[0036]

[0037] In the formula, Pti,j represents active power of the tie line with the first node being the subsystem k node i and the last node being the subsystem κ node j at the t period, Qti,j represents reactive power of the tie line at the t period; V t,k,i and θ t,k,i respectively represent voltage amplitude and phase angle of the subsystem k node i at the t period; Pti,j represents and V t,k,i , V t,κ,j , θ t,k,i , θ t,κ,j mapping relationship; Pti,j represents and V t,k,i , V t,κ,j , θ t,k,i , θ t,κ,j mapping relationship; Pti,j represents and V t,κ,j , V t,k,i , θ t,κ,j , θ t,k,i mapping relationship; Pti,j represents and V t,κ,j , V t,k,i , θ t,κ,j , θ t,k,i mapping relationship; Ω T represents a scheduling period index set; Ω R represents an electric-thermal combined subsystem index set; Pti,j represents a subsystem k boundary node index set; Represents the subsystem index set interconnected with subsystem k node i; Represents the index of subsystem κ node interconnected with subsystem k node i.

[0038] This embodiment uses the line tearing method to perform regional decomposition on the multi-region interconnected electric and thermal combined system, decomposing it into multiple independent regional electric and thermal combined subsystems and multiple tie lines. The independent regional subsystems are connected to equivalent generators at the boundary nodes. The equivalent generator power is the tie line power. The coordinated coupling relationship is as follows:

[0039]

[0040]

[0041] Where, They represent the active and reactive power of the equivalent generator x at node i in subsystem k during period t respectively; They represent the active and reactive power of tie line x at node i in subsystem k during period t respectively; represents the network loss of node i in subsystem k during period t; The network loss of the node in the region k connected to the subsystem k node i in period t (in period t, the active power flow direction of the line between this node and the subsystem k node i is from this node to the subsystem k node i) is the sum of the network loss transferred to the subsystem k node i through the incoming line and the incoming line network loss, which is recorded as the internal network loss of the subsystem k node i in period t; The coefficient representing the network loss of node j in subsystem κ transferred through tie line y of node j in subsystem κ to the node connected to it through this tie line; represents the network loss of the nodes connected to the node i in the subsystem k through the node tie line x during period t; It represents the network loss transferred from the tie line x of subsystem k node i and the interconnected nodes of the tie line when the subsystem k node i acts as the sending node during period t; It represents the network loss of the subsystem k node i as the receiving node transferred to the sending node connected to it through the subsystem k node i tie line x during period t; represents the carbon flow density of the tie line x at node i in subsystem k during period t; Represents the index set of the contact line of node i in subsystem k; Indicates the region index where the node i of subsystem k is connected through the node tie line x; represents the index of the tie line x of node i in subsystem k at node j in subsystem κ; The set of region indices that receive power transmitted by node i of subsystem k during period t; represents the set of subsystem κ node indices that receive the electric energy sent out of the region by subsystem k node i during period t; represents the set of tie-line indexes of subsystem k node i sending out power to outside area at time t; represents the set of node indexes of subsystem k sending out power to outside area at time t; represents the set of node indexes of subsystem k receiving power from outside area at time t; represents the set of tie-line indexes of subsystem k node i receiving power from outside area at time t.

[0042] S2: According to the area decomposition result, a multi-area interconnected electric-thermal combined system low-carbon economic dispatching model considering carbon emission flow and demand response is established based on a target cascade analysis method, and the model includes a superior main problem corresponding to a superior dispatching center and a plurality of inferior sub-problems respectively corresponding to each regional subsystem.

[0043] The superior dispatching center of the application refers to a superior management platform for managing and dispatching a plurality of regional subsystems.

[0044] In the embodiment, the multi-area interconnected electric-thermal combined system low-carbon economic dispatching model considering carbon emission flow and demand response includes the following coordination variables: tie-line active and reactive power, boundary node voltage amplitude and phase angle, boundary node loss, internal loss, tie-line interconnected node loss, coefficient of loss transferred from the receiving end node loss to the sending end node, loss transferred from the sending end node to the receiving end node, loss transferred from the receiving end node to the interconnected node, and node tie-line carbon flow density.

[0045] The inferior sub-problems corresponding to each regional subsystem are double-layer optimization dispatching models, the upper layer is a subsystem low-carbon economic dispatching model, and the lower layer is a load side demand response model.

[0046] 1. The subsystem low-carbon economic dispatching model in the double-layer optimization dispatching model.

[0047] 1.1. The optimization objective of the subsystem low-carbon economic dispatching model includes: coal purchase cost, additional cost of unit deep peak regulation, unit start-stop cost, carbon trading market transaction cost, green certificate transaction cost, CCER cost (CCER represents China's certified voluntary emission reduction), wind curtailment penalty fee, deep peak regulation subsidy, deep peak regulation subsidy allocation fee, demand response cost, tie-line energy-carbon comprehensive fee, and coordination variable deviation adjustment item. It is represented as:

[0048]

[0049] In the formula, represents the upper layer subsystem low-carbon economic dispatching model optimization objective of the inferior sub-problem corresponding to the subsystem k; represents the coal purchase cost of the subsystem k; represents the additional cost of unit deep peak regulation of the subsystem k; represents the start-up and shut-down cost of the kth subsystem; represents the transaction cost of the kth subsystem in the carbon trading market; represents the green certificate transaction cost of the kth subsystem; represents the CCER cost of the kth subsystem; represents the penalty cost of curtailment of wind power of the kth subsystem; represents the deep peak regulation subsidy of the kth subsystem; represents the cost of deep peak regulation subsidy allocation of the kth subsystem; represents the demand response cost of the kth subsystem; represents the energy-carbon integrated cost of the kth subsystem in the tie-line; represents the coordination variable deviation adjustment term of the kth subsystem.

[0050] (1) Coal purchasing cost.

[0051]

[0052] wherein, c COAL represents the unit coal price; represents the fitting coefficient of the active power consumption characteristic of the xth thermal power unit of the kth subsystem, represents the active power of the kth thermal power unit at the tth time period; represents the fitting coefficient of the active power consumption characteristic of the xth thermal power unit of the vth thermal power plant of the kth subsystem, represents the active power of the xth thermal power unit of the vth thermal power plant at the tth time period, represents the extraction rate of the extraction steam of the xth thermal power unit of the vth thermal power plant at the tth time period; represents the index set of thermal power units of the kth subsystem; represents the index set of thermal power plants of the kth subsystem; represents the index set of thermal power units of the vth thermal power plant of the kth subsystem.

[0053] (2) Additional cost of deep peak regulation of units.

[0054] The thermal power units that perform deep peak regulation are flexibly modified, and the participation of deep peak regulation can be divided into three stages: basic peak regulation, deep peak regulation without oil combustion support, and deep peak regulation with oil combustion support. The additional cost of deep peak regulation of units in the basic peak regulation stage is 0; the additional cost of deep peak regulation in the deep peak regulation stage without oil combustion support is the unit life loss cost; the additional cost of deep peak regulation in the deep peak regulation stage with oil combustion support is the sum of the unit life loss cost and the oil combustion cost. Therefore, the expression of the additional cost of deep peak regulation of units is:

[0055]

[0056] wherein, represents the additional cost of deep peak regulation of the xth thermal power unit of the kth subsystem at the tth time period, represents the purchasing cost of the xth thermal power unit of the kth subsystem, represents the cracking cycle of the thermal power unit rotor, represents the oil injection amount of the thermal power unit participating in the deep peak regulation unit scheduling period; c OIL is the unit oil price; represents the upper limit of the active power of the thermal power unit x in the subsystem k; represents the lower limit of the active power of the thermal power unit x in the subsystem k in the basic peak regulation stage, represents the lower limit of the active power of the thermal power unit in the non-oil injection stable combustion deep peak regulation stage, represents the lower limit of the active power of the thermal power unit in the oil injection combustion deep peak regulation stage; represents the index set of the thermal power units in the subsystem k that are flexibly reconstructed.

[0057] (3) Unit start-stop cost.

[0058]

[0059] In the formula, respectively represent the single start-stop cost of the thermal power unit x in the subsystem k, is a 0-1 variable representing whether the thermal power unit x starts at the t period, is a 0-1 variable representing whether the thermal power unit x stops at the t period; represents the single start-stop cost of the thermal power unit x in the thermal power plant v in the subsystem k, is a 0-1 variable representing whether the thermal power unit x starts at the t period, is a 0-1 variable representing whether the thermal power unit x stops at the t period.

[0060] (4) Carbon trading market transaction cost.

[0061]

[0062] In the formula, represents the carbon emission of the subsystem k; represents the carbon quota allocation amount; represents the carbon trading market base price of the subsystem k; represents the carbon emission interval length; represents the carbon trading price multiple of the increased carbon emission of the rising unit step; represents the carbon emission coefficient of the thermal power unit x in the subsystem k at the t period; Δt is the scheduling time interval, i.e., the specific duration of each scheduling period; represents the carbon emission coefficient of the thermal power unit x in the thermal power plant v in the subsystem k corresponding to the production of electric energy at the t period, represents the carbon emission coefficient of the thermal power unit x in the thermal power plant v corresponding to the production of thermal energy at the t period, represents the thermal output of the thermal power unit at time t; represents the power generation reference value of the category to which the thermal power unit x of the thermal power plant v of the subsystem k belongs, represents the peak regulation correction coefficient of the thermal power unit; represents the power generation reference value of the category to which the thermal power unit x of the thermal power plant v of the subsystem k belongs, represents the peak regulation correction coefficient of the thermal power unit, represents the heat supply reference value of the category to which the thermal power unit belongs.

[0063] (5) Green certificate transaction cost.

[0064] After the introduction of the green certificate transaction mechanism, the wind farm as a renewable energy source needs to purchase green certificates when its power generation is less than the quota, and can sell excess green certificates when its power generation is greater than the quota. Therefore, the expression of the green certificate transaction cost of the embodiment is:

[0065]

[0066] In the formula, c BGC , c SGC represents the unit price of purchasing and selling green certificates; α PUN is the penalty coefficient when the power generation of the wind farm is less than the quota; represents the renewable energy quota coefficient of the subsystem k in the total dispatching period; is the active power of the wind farm of the subsystem k at time t; represents the active power of the electrical load l of the subsystem k at time t; represents the electrical load index set of the subsystem k.

[0067] (6) CCER cost.

[0068]

[0069] In the formula, c CCER represents the CCER price; ψ CCER,WIND represents the conversion coefficient of the wind power converted into voluntary emission reduction.

[0070] (7) Wind curtailment penalty cost.

[0071]

[0072] In the formula, represents the wind curtailment penalty coefficient of the subsystem k; is the wind power prediction output of the wind farm of the subsystem k at time t.

[0073] (8) Deep peak regulation subsidy.

[0074]

[0075] Where, represents the deep peak regulation subsidy of thermal power unit x in subsystem k during period t; It represents the unit subsidy for deep peak regulation of the thermal power unit during period t.

[0076] (9) Deep peak load regulation subsidy sharing costs.

[0077]

[0078] Where, represents the apportioned cost of deep peak load regulation subsidy for thermal power unit x of subsystem k during period t; represents the deep peak regulation subsidy allocation cost of wind farm in subsystem k during period t; It represents the total deep peak load regulation subsidy apportionment cost in period t; represents the allocation factor of thermal power unit x in subsystem k during period t. and hour, is 0, otherwise The ratio of the rated capacity of the unit to the sum of the capacities of all thermal power units and wind farms in the system that need to pay subsidy sharing fees; It represents the apportionment factor of subsystem k in period t, which is the ratio of the wind farm capacity to the sum of the capacities of all thermal power units and wind farms in the system that need to pay subsidy sharing fees.

[0079] (10) Demand response costs.

[0080]

[0081] Where, It represents the cost price of subsystem k responding to unit electrical load; is the transfer amount of transferable electric load l of subsystem k in time period t; is the cost price of subsystem k responding to unit heat load; is the transfer amount of transferable heat load l of heat network υ of subsystem k in period t; is the index set of transferable electric loads of subsystem k; Represents the subsystem k-heat network index set; is the transferable heat load index set of subsystem k heat network υ.

[0082] (11) Comprehensive energy-carbon costs of interconnection lines.

[0083] The power generation side produces electricity to meet user needs. It is unfair for the power generation side to bear all the responsibility for carbon emissions. The present invention conducts carbon emission trading in the carbon emission trading market based on the actual carbon emissions generated by the power generation side, and uses the carbon emission flow to share the carbon emission responsibility. The power generation side charges carbon emission fees to users based on the transfer of carbon emission responsibility. The electricity fee and the carbon emission fee constitute the energy-carbon comprehensive fee. The optimization objective of the subsystem low-carbon economic scheduling model of the lower-level sub-problem includes the interconnection line energy-carbon comprehensive fee corresponding to the transfer of electricity and carbon emissions between subsystems through the interconnection line. The expression of the interconnection line energy-carbon comprehensive fee of this embodiment is:

[0084]

[0085] Where, represents the comprehensive energy-carbon cost of tie line x at node i in subsystem k during period t; They represent the electricity price and carbon tax price in period t respectively.

[0086] (12) Coordination variable deviation adjustment item.

[0087]

[0088] Where, represents the deviation adjustment item corresponding to the tie line x of node i in subsystem k during period t, λ t,k,i,x 、μ t,k,i,x Respectively represent the coefficient vectors of the quadratic and linear terms of the deviation adjustment term, The quadratic coefficients of the deviation adjustment terms corresponding to the following coordination variables: tie-line active and reactive power, boundary node voltage amplitude and phase angle, boundary node network loss and internal network loss, tie-line interconnection node network loss, coefficient of network loss transferred from receiving node network loss to sending node, network loss transferred from sending node to tie-line and interconnection node network loss, network loss transferred from receiving node network loss to interconnection node, and node tie-line carbon flow density; The first-order coefficients of the deviation adjustment terms corresponding to the following coordination variables: tie-line active and reactive power, boundary node voltage amplitude and phase angle, boundary node network loss, internal network loss, tie-line interconnection node network loss, coefficient of network loss transferred from receiving node network loss to sending node, network loss transferred from sending node to tie-line and interconnection node network loss, network loss transferred from receiving node network loss to interconnection node, and carbon flow density of node tie line; Z t,k,i,x represents the sub-problem coordination variable vector corresponding to the contact line x of node i in subsystem k during period t, It represents the optimized value vector of the coordination variables of the main problem corresponding to the tie line x of node i in subsystem k during period t issued by the superior dispatching center; the symbol e represents the Hadamard product.

[0089] 1.2, the constraint conditions of the subsystem low-carbon economic dispatch model, including: node balance constraint, unit constraint, node voltage constraint, line capacity constraint, heat system constraint, electric boiler constraint, node loss constraint, carbon emission flow constraint, tie line constraint.

[0090] (1) Node balance constraint.

[0091]

[0092] In the formula, Pti (k) represents the active power output of the generator at subsystem k node i at time period t, Pti (k) represents the active power load at the node at time period t, Qti (k) represents the reactive power output of the generator at the node at time period t, Qti (k) represents the reactive power load at the node at time period t; Yij (k) represents the real part of the element in the i-th row and j-th column of the admittance matrix of subsystem k node, Yij (k) represents the imaginary part of the element; N (k) represents the index set of subsystem k node.

[0093] (2) Unit constraint.

[0094] The unit constraint includes the upper and lower limits of the electric power output of the thermal power unit, the upper and lower limits of the electric power output of the thermal power unit, the upper and lower limits of the power output of the wind farm, the power factor constraint of the wind farm, the upper and lower limits of the heating steam extraction rate of the thermal power unit, the electric-thermal coupling constraint, the climbing and sliding constraint of the thermal power unit, the minimum operation time constraint of the thermal power unit, the minimum operation time constraint of the thermal power unit, the minimum shutdown time constraint of the thermal power unit, and the minimum shutdown time constraint of the thermal power unit.

[0095] (2.1) Upper and lower limits of the electric power output of the thermal power unit:

[0096]

[0097] In the formula, Xti (k, x) is a 0-1 variable representing the start-stop state of the thermal power unit x of subsystem k at time period t; Qti (k, x) represents the reactive power of the thermal power unit x of subsystem k at time period t; Pti (k, x) represents the upper and lower limits of the active power of the thermal power unit x of subsystem k; Pti (k, x) represents the upper and lower limits of the active power of the thermal power unit x of subsystem k.

[0098] (2.2) Upper and lower limits of the electric power output of the thermal power unit:

[0099]

[0100] In the formula, is a 0-1 variable representing the on-off state of thermal power unit x in thermal power plant v of subsystem k at time period t; is the reactive power of thermal power unit x in thermal power plant v of subsystem k at time period t; is the upper limit of active power of thermal power unit x in thermal power plant v of subsystem k at time period t, respectively; is the upper limit of reactive power of thermal power unit x in thermal power plant v of subsystem k, respectively.

[0101] (2.3) Upper and lower limits of wind farm output:

[0102]

[0103] (2.4) Power factor constraint of wind farm:

[0104]

[0105] wherein, is the power factor of wind farm of subsystem k at time period t; is the upper limit of power factor of wind farm of subsystem k, respectively.

[0106] (2.5) Upper and lower limits of heat supply extraction rate of thermal power unit:

[0107]

[0108] wherein, is the heat supply extraction rate of thermal power unit x in thermal power plant v of subsystem k at time period t; is the upper limit of heat supply extraction rate of thermal power unit x in thermal power plant v of subsystem k, respectively.

[0109] (2.6) Electric-thermal coupling constraint:

[0110]

[0111] wherein, is the upper limit of active power of thermal power unit x in thermal power plant v of subsystem k at pure condensing condition, respectively; is the power supply and heat supply ratio of thermal power unit x in thermal power plant v of subsystem k at maximum condensing gas condition; is the power supply and heat supply ratio of thermal power unit x in thermal power plant v of subsystem k at back pressure condition; ΔH is the steam enthalpy drop.

[0112] (2.7) Ramp constraint of thermal power unit:

[0113]

[0114] wherein, is the maximum unit start-up ramp rate of thermal power unit x of subsystem k; represents the maximum non-shutdown ramp rate of the thermal power plant v for the thermal power unit x of the subsystem k. represents the maximum shutdown ramp rate of the thermal power plant v for the thermal power unit x of the subsystem k. represents the maximum non-shutdown ramp rate of the thermal power plant v for the thermal power unit x of the subsystem k.

[0115] (2.8) Thermal power unit ramping and ramp-down constraints:

[0116]

[0117] wherein, represents the maximum startup ramp rate of the thermal power plant v for the thermal power unit x of the subsystem k. represents the maximum non-startup ramp rate of the thermal power plant v for the thermal power unit x of the subsystem k. represents the maximum shutdown ramp rate of the thermal power plant v for the thermal power unit x of the subsystem k. represents the maximum non-shutdown ramp rate of the thermal power plant v for the thermal power unit x of the subsystem k.

[0118] (2.9) Minimum up-time constraint for thermal power units:

[0119]

[0120] wherein, represents the continuous up-time of the thermal power plant v for the thermal power unit x of the subsystem k before the (t-1) period including the (t-1) period; represents the minimum up-time of the thermal power plant v for the thermal power unit x of the subsystem k.

[0121] (2.10) Minimum up-time constraint for thermal power units:

[0122]

[0123] wherein, represents the continuous up-time of the thermal power plant v for the thermal power unit x of the subsystem k before the (t-1) period including the (t-1) period; represents the minimum up-time of the thermal power plant v for the thermal power unit x of the subsystem k.

[0124] (2.11) Minimum shutdown time constraint for thermal power units:

[0125]

[0126] wherein, represents the continuous shutdown time of the thermal power plant v for the thermal power unit x of the subsystem k before the (t-1) period including the (t-1) period; represents the minimum shutdown time of the thermal power plant v for the thermal power unit x of the subsystem k.

[0127] (2.12) Minimum shutdown time constraint for thermal power units:

[0128]

[0129] wherein, denotes the consecutive downtime of subsystem k, thermal power plant v, thermal power unit x before time period (t-1) including time period (t-1); denotes the minimum downtime of subsystem k, thermal power plant v, thermal power unit x.

[0130] (3) Node voltage constraints.

[0131]

[0132] wherein, V k,i denote the upper and lower limits of the amplitude of the voltage of node i in subsystem k, respectively; θ k,i denote the upper and lower limits of the phase angle of the voltage of node i in subsystem k, respectively.

[0133] (4) Line capacity constraints.

[0134]

[0135] wherein, denotes the apparent power of branch h in subsystem k at time period t, denote the active and reactive power of the branch at time period t, respectively, denote the upper and lower limits of the apparent power of the branch, respectively; denotes the index set of branches in subsystem k.

[0136] (5) Thermal system constraints.

[0137] The thermal system takes into account the attenuation, delay, and heat storage characteristics of the heat network pipeline. The thermal system constraints include: heat network mixing point mixed temperature constraints, pipeline outlet temperature constraints, pipeline heat power loss constraints, pipeline heat storage constraints, heat source node heat output and temperature relationship constraints, heat load node heat power and temperature relationship constraints, and heat network node temperature upper and lower limit constraints.

[0138] (5.1) Heat network mixing point mixed temperature constraints:

[0139]

[0140] wherein, denote the mass flow rate and temperature of the jth input heat medium of mixing point i in the heat network of subsystem k at time period t; denote the mass flow rate and temperature of the jth output heat medium of mixing point i in the heat network of subsystem k at time period t; denotes the index set of mixing points in the heat network of subsystem k; denotes the set of indices of the input and output heat carrier of subsystem k heat network υ at mixing point i.

[0141] (5.2) Pipe outlet temperature constraint

[0142] The heat carrier groups of all pipes are divided by time period, and the expression of pipe outlet temperature constraint is:

[0143]

[0144] where x∈{S, R}; S denotes the supply water network; R denotes the return water network; denotes the temperature of the heat carrier group at the outlet of pipe h of subsystem k heat network υ at time period t considering the attenuation characteristics, denotes the mass flow rate of the heat carrier of the pipe at time period t; if the conditions of t2=0, then denotes the temperature of the heat carrier group at the inlet of pipe h of subsystem k heat network υ at time period t-t2, which is at time period t-t1; denotes the temperature of the heat carrier group at the inlet of the pipe at time period t, which belongs to the part of the heat carrier group at the outlet of the pipe at time period t; denotes the index of the last heat carrier group of all the heat carrier groups flowing out of and flowing through pipe h of subsystem k heat network υ at time period t and time period t-1, respectively; denotes the total mass of the heat carrier flowing into pipe h of subsystem k heat network υ from time period t1 to time period t; when denotes the total mass of the heat carrier flowing into pipe h of subsystem k heat network υ from time period t1 to time period t; when denotes the total mass of the heat carrier flowing into pipe h of subsystem k heat network υ from time period t1 to time period t; when denotes the total mass of the heat carrier flowing into pipe h of subsystem k heat network υ from time period t1 to time period t; when denotes the total mass of the heat carrier flowing into pipe h of subsystem k heat network υ from time period t1 to time period t; when denotes the total mass of the heat carrier flowing into pipe h of subsystem k heat network υ from time period t1 to time period t; when denotes the total mass of the heat carrier flowing into pipe h of subsystem k heat network υ from time period t1 to time period t; when denotes the total mass of the heat carrier flowing into pipe h of subsystem k heat network υ from time period t1 to time period t; when denotes the ambient temperature of subsystem k at time period t; denotes the heat transfer coefficient of pipe h of subsystem k heat network υ; ρ HM denotes the density of the heat carrier; denotes the cross-sectional area of pipe h of subsystem k heat network υ, denotes the length of the pipe; c HM denotes the specific heat capacity of the heat carrier; N denotes the set of natural numbers; denotes the set of indices of pipe h of subsystem k heat network υ.

[0145] (5.3) Pipe heat power loss constraint:

[0146]

[0147] where represents the heat power loss of the heat pipe h of the heat network υ of the subsystem k at time t.

[0148] (5.4) Heat pipe storage constraint:

[0149]

[0150] wherein, represents the heat storage amount of the heat pipe h of the heat network υ of the subsystem k at time t, represents the heat storage change amount of the heat pipe at time t; represents the heat medium temperature at the inlet of the heat pipe h of the heat network υ of the subsystem k at time t.

[0151] (5.5) Heat source node heat output and temperature relationship constraint:

[0152]

[0153] wherein, represents the heat power of the heat source i of the heat network υ of the subsystem k at time t, represents the heat medium mass flow rate flowing through the heat source at time t; respectively represent the heat medium temperatures at the inlet and outlet of the heat source at time t; represents the heat source node index set of the heat network υ of the subsystem k.

[0154] (5.6) Heat load node heat power and temperature relationship constraint:

[0155]

[0156] wherein, represents the heat load power of the heat load i of the heat network υ of the subsystem k at time t, represents the heat medium mass flow rate flowing through the heat load at time t; respectively represent the heat medium temperatures at the inlet and outlet of the heat load at time t; represents the heat load node index set of the heat network υ of the subsystem k.

[0157] (5.7) Heat network node temperature upper and lower limit constraint:

[0158]

[0159] wherein, respectively represent the upper and lower limits of the heat medium temperature at the outlet of the heat source i of the heat network υ of the subsystem k at time t; respectively represent the upper and lower limits of the heat load inlet temperature of the heat load i of the heat network υ of the subsystem k at time t, respectively represent the upper and lower limits of the heat load outlet temperature.

[0160] (6) Electric boiler constraint.

[0161]

[0162] wherein, respectively represent the thermal power, active electric power of the electric boiler l of the thermal power plant v of the subsystem k at time t, represents the electric-thermal conversion efficiency of the electric boiler, represents the capacity of the electric boiler, represents the start-stop state of the electric boiler at time t; represents the index set of the electric boilers of the thermal power plant v of the subsystem k.

[0163] (7) Node network loss constraint.

[0164]

[0165] wherein, represents the coefficient of the network loss of the subsystem k node j at time t, which is transferred to the subsystem k node i through the incoming line ij; represents the network loss of the subsystem k branch ij at time t; represents the active power of the subsystem k branch ij at time t; represents the set of outgoing line end nodes within the region of the subsystem k node i at time t; represents the set of incoming line start nodes within the region of the subsystem k node j at time t.

[0166] (8) Carbon emission flow constraint.

[0167] The carbon emission flow constraint includes: power system node carbon flow balance constraint, heat network pipeline carbon emission flow constraint, heat network mixing point carbon potential constraint, heat source node carbon flow density equality constraint, and heat load node carbon flow density equality constraint.

[0168] (8.1) Power system node carbon flow balance constraint:

[0169]

[0170] wherein, represents the carbon flow density of the subsystem k branch ij at time t; represents the carbon emission intensity of the generator of the subsystem k node j at time t; represents the carbon potential of the subsystem k node j at time t.

[0171] (8.2) Heat network pipeline carbon emission flow constraint:

[0172]

[0173] wherein, respectively represent the carbon flow density of the input end, output end, and energy storage of the heat network υ pipeline h of the subsystem k at time t.

[0174] (8.3) Carbon potential constraints at mixing points of heat networks:

[0175]

[0176] where, denotes the carbon potential of subsystem k at heat network υ mixing point i at time period t; denotes the jth input heat medium carbon flow density of subsystem k at heat network υ mixing point i at time period t; denotes the jth output heat medium carbon flow density of subsystem k at heat network υ mixing point i at time period t.

[0177] (8.4) Carbon flow density equality constraints at heat source nodes:

[0178]

[0179] where, denotes the carbon flow density of heat source output, input end of subsystem k at heat network υ heat source node i at time period t, respectively; denotes the carbon flow rate injected by the heat source at time period t; denotes the carbon emission intensity, heat power of heat engine unit j at node i of subsystem k at time period t, respectively; denotes the carbon potential of the node of the power system where the electric boiler j is located at node i of subsystem k at time period t, denotes the electric power of the electric boiler at time period t; denotes the index set of heat engine units, electric boilers at heat source node i of subsystem k at heat network υ, respectively.

[0180] (8.5) Carbon flow density equality constraints at heat load nodes:

[0181]

[0182] where, denotes the carbon flow density of heat load output, input end of subsystem k at heat network υ heat load node i at time period t, respectively.

[0183] (9) Tie line constraints.

[0184]

[0185] where, denotes the upper limit of active power transmission of tie line x of node i of subsystem k; denotes the upper and lower limits of reactive power of tie line x of node i of subsystem k, respectively.

[0186] 2. Load side demand response model in the double-layer optimal dispatching model.

[0187] 2.1, the optimization objective of the lower-level load-side demand response model, including: energy-carbon comprehensive cost, demand response cost, demand response subsidy, expressed as:

[0188]

[0189] In the formula, indicates the lower-level load-side demand response model optimization objective of the subsystem k corresponding to the lower-level sub-problem; indicates the energy-carbon comprehensive cost that the load side of the subsystem k needs to pay; indicates the demand response subsidy of the subsystem k.

[0190] (1) Energy-carbon comprehensive cost that the load side needs to pay:

[0191]

[0192] In the formula, indicates the heat price at t period.

[0193] (2) Demand response subsidy:

[0194]

[0195] In the formula, indicates the subsidy of the subsystem k responding to unit electric and heat load, respectively.

[0196] 2.2, the constraint condition of the lower-level load-side demand response model, expressed as:

[0197]

[0198] In the formula, is a 0-1 variable indicating that the transferable electric load l of the subsystem k at t period is transferred in or out, indicates the transfer-in or transfer-out amount of the transferable electric load at t period, indicates the proportion of the maximum transfer amount of the transferable electric load at t period, indicates the active load amount of the transferable electric load before participating in demand response; is a 0-1 variable indicating that the transferable heat load l of the heat network υ of the subsystem k at t period is transferred in or out, indicates the transfer-in or transfer-out amount of the transferable heat load at t period, indicates the proportion of the maximum transfer amount of the transferable heat load at t period, indicates the heat load amount of the transferable heat load before participating in demand response.

[0199] Step 3, linearize the model and solve the linearized model.

[0200] Step 3: solving the linearized model by iteration, including:

[0201] 1st iteration: (1) the upper dispatching center sends the initial values of the master problem coordination variables of each regional subsystem to the corresponding regional subsystem; (2) each regional subsystem optimizes the sub-problem of the regional subsystem according to the received initial values of the master problem coordination variables, and uploads the optimized values of the sub-problem coordination variables to the upper dispatching center; (3) the upper dispatching center optimizes the master problem according to the optimized values of the sub-problem coordination variables uploaded by each regional subsystem, and obtains the optimized values of the master problem coordination variables of each regional subsystem; (4) adjust the deviation adjustment term coefficient in the optimization objective function to continue the next iteration;

[0202] (τ+1)th iteration: (1) the upper dispatching center sends the optimized values of the master problem coordination variables obtained in the τth iteration to the corresponding regional subsystem; (2) each regional subsystem optimizes the sub-problem of the regional subsystem according to the received optimized values of the master problem coordination variables, and uploads the optimized values of the sub-problem coordination variables obtained in the (τ+1)th iteration to the upper dispatching center; (3) the upper dispatching center optimizes the master problem according to the optimized values of the sub-problem coordination variables uploaded by each regional subsystem in the (τ+1)th iteration, and obtains the optimized values of the master problem coordination variables of each regional subsystem; (4) according to the optimization results of the upper master problem and the lower sub-problem, it is judged whether to converge: if it converges, the solving is ended, otherwise the deviation adjustment term coefficient of the coordination variable in the optimization objective function is adjusted to continue the next iteration; where τ is a natural number greater than 0.

[0203] In each iteration, when each regional subsystem optimizes the sub-problem in step (2), it includes optimizing the upper subsystem low-carbon economic dispatching model and optimizing the load side demand response model: after the upper layer completes the optimization, it transmits the optimized values of the carbon potential of each load node to the lower layer, and the lower layer optimizes according to the received values of the carbon potential of each load node, and transmits the optimized electric and thermal loads to the upper layer for iterative solving; when the difference between the current and previous two electric and thermal load transfer amounts is less than the error limit, the optimization of the sub-problem in this iteration is ended.

[0204] Wherein, the upper dispatching center optimizes the master problem by making the master problem coordination variables as close as possible to the optimized values of the sub-problem coordination variables. The optimization objective of the upper master problem is as follows:

[0205]

[0206] Wherein, C MAIN represents the upper master problem optimization objective; represents the master problem deviation adjustment term corresponding to the tie line x of the subsystem k node i at the t period; represents the master problem coordination variable corresponding to the tie line x of the subsystem k node i at the t period; It represents the optimized value vector of the coordination variables of the subproblem corresponding to the contact line x of node i in the subsystem k during period t; They represent the main problem coordination variables corresponding to the active and reactive powers of tie line x at node j in subsystem k during period t; They represent the main problem coordination variables corresponding to the voltage amplitude and phase angle of node i in subsystem k during period t; represents the coordination variable of the main problem corresponding to the network loss of node i in subsystem k during period t; represents the coordination variable of the main problem corresponding to the internal network loss of node i in subsystem k during period t; The main problem coordination variable corresponding to the coefficient of the network loss of subsystem κ node j transferred through the subsystem κ node j tie line y to the node connected to this node through this tie line; represents the coordination variable of the main problem corresponding to the network loss of the node i in the subsystem k during period t, which is connected through the node tie line x; The coordination variable of the main problem represents the network loss corresponding to the network loss transferred from the tie line x of the subsystem k-node i and the interconnected nodes of the tie line during the period t. The coordination variable of the main problem represents the network loss of the subsystem k node i as the receiving node transferred to the sending node through the subsystem k node i tie line x during period t; represents the coordination variable of the main problem corresponding to the carbon flow density of the tie line x of the subsystem k node i during period t; It represents the optimized value of the active power of tie line x at node j in subsystem k during period t in the lower-level subproblem.

[0207] The constraints of the upper-level main problem are the coordination variable constraints of the main problem:

[0208]

[0209] When judging whether the solution has converged in step (4) of the (τ+1)th iteration, the convergence criterion is as follows:

[0210]

[0211] Where, Indicates the τth iteration Indicates the τth iteration express An element of , which is the optimized value of a single master problem coordination variable in the τth iteration; express An element of , which is the optimized value of the coordination variable of a single subproblem in the τth iteration, represents the upper limit of the coordination variable; represents the optimization value of the upper-level subsystem low-carbon economic dispatch model corresponding to the lower-level sub-problem of subsystem k in the τth iteration; represents the value of the coordination variable bias adjustment term of subsystem k after the optimization of the lower-level sub-problem corresponding to subsystem k in the τth iteration; ε1, ε2 represent error limits.

[0212] The adjusted coordination variable bias adjustment term coefficient is:

[0213]

[0214] In the formula, λ t,k,i,x (τ), μ t,k,i,x (τ) respectively represent the quadratic term and the linear term coefficient vector of the bias adjustment term corresponding to the tie line x of the node i of the subsystem k in the t period in the τth iteration, and α λ represents a vector composed of constant numbers that affect the quadratic term coefficient of the bias adjustment term in adjacent iterations.

[0215] Step 4: According to the optimization results, the carbon emission responsibility is allocated, and the carbon emission allocation results of each regional subsystem (i.e. each regional subsystem of the electric-thermal combined system) are obtained.

[0216] After the model solving in step 3 is completed, the optimized values of each variable of each regional subsystem and each tie line are known, and at this time, the carbon flow rate that each economic subject needs to bear can be calculated.

[0217] The generator network loss contribution of each node is calculated:

[0218]

[0219] In the formula, P represents the network loss contribution of the generator of the node i of the subsystem k in the t period; when there is more than one generator in the node i of the subsystem k, the network loss contribution of any generator of the node in the t period is the product of the proportion of the active power of the generator in the sum of the active powers of all generators of the node and .

[0220] After obtaining the network loss contribution of each thermal power unit and thermal power unit, the carbon flow rate caused by the active loss that each unit needs to bear is calculated:

[0221]

[0222] In the formula, P respectively represent the carbon flow rate caused by the active loss that the thermal power unit x of the thermal power plant v of the subsystem k needs to bear in the t period and the network loss contribution; represents the carbon flow rate caused by the active loss that the thermal power unit x of the thermal power plant v of the subsystem k needs to bear in the t period and the network loss contribution.

[0223] For any power system node that is not connected to an electric boiler, calculate the carbon flow rate that the node's electrical load needs to bear:

[0224]

[0225] For the power system node connected to the electric boiler, calculate the carbon flow rate that the electric load needs to bear, excluding the electric boiler power:

[0226]

[0227] Where, represents the carbon flow rate that the electrical load of node i in subsystem k needs to bear during period t; represents the carbon flow rate that the electric load of node i in subsystem k, excluding the electric power of the electric boiler, needs to bear during period t; represents the active power of electric boiler x at node i in subsystem k during period t; The node index set representing the electric boiler connected to the power system of subsystem k; Represents the index set of electric boiler in subsystem k node i.

[0228] According to the heat network power flow and the proportional sharing principle, the heat network loss is allocated to each heat source, and then the carbon flow rate caused by the heat network loss that each heat source needs to bear is calculated:

[0229]

[0230] Where, They represent the carbon flow rate and heat network loss contribution caused by the heat network loss that the subsystem k heat power plant v heat power unit x needs to bear during period t; They represent the carbon flow rate and heat network loss contribution caused by the heat network loss that the electric boiler x at node i in subsystem k needs to bear during period t, Indicates the electric-to-heat conversion efficiency of the electric boiler.

[0231] Calculate the carbon flow rate required for each heat load:

[0232]

[0233] Where, It represents the carbon flow rate that the heat load of node i in the heat network υ of subsystem k needs to bear during period t.

[0234] The present invention also provides a low-carbon economic dispatch system for a multi-area interconnected electric and thermal combined system considering carbon emission flow and demand response, referring to Figure 2As shown, it comprises: a regional data processing module, a main problem optimization module, a convergence determination module; each regional subsystem is configured with a regional data processing module, which is composed of an upper optimization module, a lower optimization module, a carbon flow rate calculation module, and a result output module; the upper optimization module is used to optimize the upper subsystem low-carbon economic dispatch model of the lower problem; the lower optimization module is used to optimize the lower load side demand response model; the carbon flow rate calculation module is used to calculate the carbon flow rate allocated by each economic subject according to the optimization result; the result output module is used to output the optimization result; the main problem optimization module is used to optimize the upper problem; the convergence determination module is used to determine whether the model converges.

[0235] When the system starts running, the convergence determination module transmits the initial value of the adjustment bias adjustment term coefficient to the master problem optimization module of the upper dispatching center and the regional data processing module of each regional subsystem. The master problem optimization module transmits the initial value of each master problem coordination variable to each regional data processing module. After receiving the initial value of each master problem coordination variable, each regional data processing module optimizes the lower-level sub-problems through the upper and lower optimization modules therein. Specifically, after the upper optimization module completes the optimization of the lower-level sub-problems of the upper-level subsystem low-carbon economic dispatching model, it transmits the optimized carbon potential values of each load node to the lower level. The lower optimization module optimizes according to the received carbon potential values of each load node, and transmits the optimized electric and thermal load adjustment to the upper level. Iterative solving is performed until the difference between the electric and thermal load adjustment before and after is less than the error limit, and the optimization of the lower-level sub-problems is completed. After each regional data processing module completes the optimization of the lower-level sub-problems for the first time, it uploads the optimized values of the sub-problem coordination variables to the master problem optimization module, and transmits the difference between the optimized values of the sub-problem coordination variables and the adjustment bias adjustment term of the optimization target of the upper-level subsystem low-carbon economic dispatching model of the lower-level sub-problems to the convergence determination module. After receiving the optimized values of the sub-problem coordination variables, the master problem optimization module optimizes the upper-level master problem, and transmits the optimized values of the master problem coordination variables to each regional data processing module and the convergence determination module. At this time, it is the first iteration, so the convergence determination module does not determine whether to converge. After adjusting the adjustment bias adjustment term coefficient, it is transmitted to the master problem optimization module and each regional data processing module to complete the first iteration. After each regional data processing module receives the optimized values of the master problem coordination variables transmitted by the master problem optimization module and the latest adjustment bias adjustment term coefficient transmitted by the convergence determination module, it optimizes the lower-level sub-problems, uploads the optimized values of the sub-problem coordination variables to the master problem optimization module, and transmits the difference between the optimized values of the sub-problem coordination variables and the adjustment bias adjustment term of the optimization target of the lower-level sub-problems to the convergence determination module. After receiving the optimized values of each sub-problem coordination variable, the master problem optimization module optimizes the upper-level master problem, and transmits the optimized values of the master problem coordination variables to each regional data processing module and the convergence determination module. The convergence determination module determines whether to converge according to the known data. If it converges, the solving is completed. If it does not converge, the adjustment bias adjustment term coefficient is continuously adjusted and transmitted to the master problem optimization module and each regional data processing module to complete the second iteration. Each subsequent iteration is the same as the second iteration, and the solving is completed until convergence. After the solving is completed, the carbon flow rate calculation module of each regional data processing module calculates the carbon flow rate that each economic subject needs to bear according to the optimized results, and the result output module of each regional data processing module outputs the optimized results and the carbon emission allocation results.

[0236] The above embodiments are preferred embodiments of the present application, and those of ordinary skill in the art can make various changes or improvements on the basis of the above embodiments without departing from the general concept of the present application. These changes or improvements should all fall within the scope of the present application.

Claims

1. A low-carbon economic dispatch method for a multi-region interconnected electric and thermal system considering carbon emission flows and demand response, characterized in that: include: Step 1: Analyze the interconnection line power flow and decompose the multi-region interconnected electric and thermal system into multiple independent regional subsystems. Each independent regional subsystem is connected to other independent regional subsystems at the boundary nodes to analyze the coordinated coupling relationship. Step 2: Based on the regional decomposition results and using the target cascade analysis method, a low-carbon economic dispatch model for a multi-region interconnected electric and thermal system is established that considers carbon emission flows and demand response. The model includes a superordinate main problem corresponding to the superior dispatch center and several subordinate subproblems corresponding to each regional subsystem. Step 3: linearize the model and solve the linearized model; Step 4: Allocate carbon emission responsibilities according to the optimization results to obtain the carbon emission allocation results of each regional subsystem.

2. The method according to claim 1, characterized in that The multi-region interconnected electric and thermal combined system is decomposed into regions through the line tearing method. The two independent regional subsystems are connected to each other through boundary nodes and tie lines. When analyzing the coordinated coupling relationship of each independent regional subsystem in the combined system, each tie line of the regional subsystem is equivalent to the corresponding equivalent generator.

3. The method according to claim 1, characterized in that For the tie line connecting subsystem k and subsystem κ, with the nodes at both ends being node i of subsystem k and node j of subsystem κ, the tie line flow is as follows: Where, represents the active power of the tie line with the first node being node i of subsystem k and the last node being node j of subsystem κ in period t, represents the reactive power of the tie line during period t; V t,k,i ,θ t,k,i They represent the voltage amplitude and phase angle of node i in subsystem k during period t respectively; express With V t,k,i 、V t,κ,j ,θ t,k,i ,θ t,κ,j Mapping relationship; express With V t,k,i 、V t,κ,j ,θ t,k,i ,θ t,κ,j Mapping relationship; express With V t,κ,j 、V t,k,i ,θ t,κ,j ,θ t,k,i Mapping relationship; express With V t,κ,j 、V t,k,i ,θ t,κ,j ,θ t,k,i Mapping relationship; Ω T Represents the scheduling period index set; Ω R Represents the index set of the electric and thermal combined subsystem; Represents the subsystem k boundary node index set; Represents the subsystem index set interconnected with subsystem k node i; Represents the index of subsystem κ node interconnected with subsystem k node i.

4. The method according to claim 1, wherein The established low-carbon economic dispatch model for the multi-region interconnected electric and thermal combined system considering carbon emission flow and demand response has coordination variables including: active and reactive power of the tie line, voltage amplitude and phase angle of the boundary node, network loss of the boundary node, internal network loss, network loss of the interconnected node of the tie line, coefficient of network loss transferred from the receiving node network loss to the sending node, network loss transferred from the sending node to the tie line and interconnected node network loss, network loss transferred from the receiving node network loss to the interconnected node, and carbon flow density of the node tie line; Step 3 solves the linearized model through iteration, including: First iteration: (1) The superior dispatching center sends the initial values ​​of the coordination variables of the main problem of each regional subsystem to the corresponding regional subsystem; (2) Each regional subsystem optimizes the subproblems of its own regional subsystem based on the received initial values ​​of the coordination variables of the main problem, and uploads the obtained optimized values ​​of the coordination variables of the subproblems to the superior dispatching center; (3) The superior dispatching center optimizes the main problem based on the optimized values ​​of the coordination variables of the subproblems uploaded by each regional subsystem, and obtains the optimized values ​​of the coordination variables of the main problem of each regional subsystem; (4) Adjust the coefficient of the deviation adjustment term in the optimization objective function to continue the next iteration; The (τ+1)th iteration: (1) The upper-level dispatching center sends the optimized values ​​of the coordination variables of each main problem obtained in the τth iteration to the corresponding regional subsystem; (2) Each regional subsystem optimizes the subproblems of its own regional subsystem based on the received optimized values ​​of the coordination variables of the main problem, and uploads the optimized values ​​of the coordination variables of the subproblems obtained in the (τ+1)th iteration to the upper-level dispatching center; (3) The upper-level dispatching center optimizes the main problem based on the optimized values ​​of the coordination variables of the subproblems obtained in the (τ+1)th iteration uploaded by each regional subsystem, and obtains the optimized values ​​of the coordination variables of the main problem of each regional subsystem; (4) Based on the optimization results of the upper-level main problem and the lower-level subproblems, it is judged whether convergence has occurred: if convergence has occurred, the solution is terminated; otherwise, the coefficient of the coordination variable deviation adjustment term in the optimization objective function is adjusted to continue the next iteration; where τ is a natural number greater than 0.

5. The method according to claim 1, wherein The lower-level subproblems corresponding to each regional subsystem are two-layer optimization scheduling models, the upper layer is the subsystem low-carbon economic scheduling model, and the lower layer is the load-side demand response model; after the upper layer completes the optimization, the optimized carbon potential optimization value of each load node is passed to the lower layer, and the lower layer optimizes according to the received carbon potential optimization value of each load node, and passes the optimized electricity and heat loads to the upper layer for iterative solution; when the difference between the current and subsequent electricity and heat load transfer amounts is less than the error limit, the iteration ends.

6. The method according to claim 5, characterized in that The upper-level subsystem low-carbon economic dispatch model has optimization objectives including: coal purchase cost, additional cost of deep peak regulation of units, unit start-up and shutdown cost, carbon trading market transaction cost, green certificate transaction cost, CCER cost, wind curtailment penalty fee, deep peak regulation subsidy, deep peak regulation subsidy sharing cost, demand response cost, interconnection line energy-carbon comprehensive cost and coordination variable deviation adjustment item. The constraints include: node power balance constraint, unit constraint, node voltage constraint, line capacity constraint, thermal system constraint, electric boiler constraint, node network loss constraint, carbon emission flow constraint and interconnection line constraint. The load-side demand response model at the lower level has optimization objectives including: energy-carbon comprehensive cost, demand response cost, demand response subsidy, and the constraints are demand response constraints.

7. The method according to claim 6, characterized in that The expression of the comprehensive energy-carbon cost of the tie line is: Where, represents the comprehensive energy-carbon cost of the interconnection line of subsystem k; represents the comprehensive energy-carbon cost of tie line x at node i in subsystem k during period t; represent the electricity price and carbon tax price in period t respectively; represents the active power of tie line x at node i in subsystem k during period t; It represents the network loss transferred from the tie line x of subsystem k node i and the interconnected nodes of the tie line when the subsystem k node i acts as the sending node during period t; represents the carbon flow density of the tie line x of node i in subsystem k during period t; Δt is the scheduling time interval; Ω represents the network loss of the receiving node in subsystem k i transferred to the sending node through the tie line x of subsystem k i; T Represents a set of scheduling period indexes; Represents the subsystem k boundary node index set; represents the index set of the contact line of node i in subsystem k; Ω R Represents the index set of the electric and thermal combined subsystem; The node index set representing the subsystem k that sends power out of the region during period t; The set of tie line indices representing the transmission of power from node i in subsystem k to outside the region during period t; The node index set representing the subsystem k receiving power from outside the area during period t; Represents the set of tie line indices for subsystem k node i receiving power from outside the area during period t.

8. The method according to claim 6, characterized in that Carbon emission flow constraints include: carbon flow balance constraints at power system nodes, carbon emission flow constraints at heat network pipelines, carbon potential constraints at heat network mixing points, carbon flow density equality constraints at heat source nodes, and carbon flow density equality constraints at heat load nodes.

9. The method according to claim 1, characterized in that Step 4 includes: using the model to solve the variables of each regional subsystem and each interconnection line to obtain the optimization results, and calculating: the carbon flow rate caused by the active power loss that each thermal power unit and cogeneration unit needs to bear, the carbon flow rate that the node electrical load needs to bear, the carbon flow rate caused by the heat network loss that each heat source needs to bear, and the carbon flow rate that each heat load needs to bear.

10. A low-carbon economic dispatching system for a multi-zone interconnected electric and thermal combined system based on the method according to any one of claims 1 to 9, characterized in that: include: Main problem optimization module, regional data processing module, convergence judgment module, and each regional subsystem is configured with a regional data processing module; The main problem optimization module is applied to the upper-level dispatching center and is used to: optimize the main problem according to the sub-problem coordination variable optimization values ​​uploaded by each regional subsystem to obtain the main problem coordination variable optimization values ​​of each regional subsystem; The regional data processing module includes: an upper-level optimization module, a lower-level optimization module, a carbon flow rate calculation module, and a result output module; the upper-level optimization module is used to optimize the upper-level subsystem low-carbon economic scheduling model of the lower-level subproblem to obtain the carbon potential optimization value of each load node; the lower-level optimization module is used to optimize the lower-level load-side demand response model based on the carbon potential optimization value of each load node obtained in the upper layer to obtain electricity and heat loads; the carbon flow rate calculation module is used to calculate the carbon flow rate shared by each economic entity based on the optimization result; the result output module is used to output the optimization result and the carbon flow rate shared by each economic entity; The convergence determination module is used to determine whether the optimization result has converged.

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